Evidence map›Paper›PMID 33165813›Full record

ArticleScience China. Life sciences2021

Lipidomic analyses reveal enhanced lipolysis in planthoppers feeding on resistant host plants.

Xiaohong Zheng, Yeyun Xin, Yaxin Peng, Junhan Shan, Ning Zhang, Di Wu, Jianping Guo, Jin Huang, Wei Guan, Shaojie Shi and 8 more

Abstract read
PubMed Publisher
In one paragraph

Article in Science China. Life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.2field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 23 citations in OpenAlex.

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  7. A neural mScience China. Life sciences · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 3 institutions in 1 country.

Xiaohong ZhengState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Yeyun XinState Key Laboratory of Hybrid Rice, Hunan Hybrid Rice Research Center, Hunan Academy of Agricultural Sciences, Changsha, 410125, China.
Yaxin PengState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Junhan ShanState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Ning ZhangState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Di WuState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Jianping GuoState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Jin HuangState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Wei GuanState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Shaojie ShiState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Cong ZhouState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Rongzhi ChenState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Bo DuState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Lili ZhuState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Fang YangState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Xiqin FuState Key Laboratory of Hybrid Rice, Hunan Hybrid Rice Research Center, Hunan Academy of Agricultural Sciences, Changsha, 410125, China.
Longping YuanState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China.
Guangcun HeState Key Laboratory of Hybrid Rice, College of Life Sciences, Wuhan University, Wuhan, 430072, China. gche@whu.edu.cn.
Wuhan University · CNHunan Academy of Agricultural Sciences · CNChina National Hybrid Rice R&D Central Hunan Hybrid Rice Reserch Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The brown planthopper (BPH) (Nilaparvata lugens Stål) is a highly destructive pest that seriously damages rice (Oryza sativa L.) and causes severe yield losses. To better understand the physiological and metabolic mechanisms through which BPHs respond to resistant rice, we combined mass-spectrometry-based lipidomics with transcriptomic analysis and gene knockdown techniques to compare the lipidomes of BPHs feeding on either of the two resistant (NIL-Bph6 and NIL-Bph9) plants or a wild-type, BPH susceptible (9311) plant. Insects that were fed on resistant rice transformed triglyceride (TG) to phosphatidylcholine (PC) and digalactosyldiacylglycerol (DGDG), with these lipid classes showing significant alterations in fatty acid composition. Moreover, the insects that were fed on resistant rice were characterized by prominent expression changes in genes involved in lipid metabolism processes. Knockdown of the NlBmm gene, which encodes a lipase that regulates the mobilization of lipid reserves, significantly increased TG content and feeding performance of BPHs on resistant plants relative to dsGFP-injected BPHs. Our study provides the first detailed description of lipid changes in BPHs fed on resistant and susceptible rice genotypes. Results from BPHs fed on resistant rice plants reveal that these insects can accelerate TG mobilization to provide energy for cell proliferation, body maintenance, growth and oviposition.

Indexed as

HerbivoryLipidomicsLipolysisAnimalsHemipteraOryzabrown planthopperlipid metabolismlipidomicsrice plantsRNA interferenceRNA-seq

Identifiers

PMID33165813
OpenAlexW3103723132

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.